
from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE				:Optional[int]									=							{
    'google/tapas-base-finetuned-sqa': (
        'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wtq': (
        'https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-wikisql-supervised': (
        'https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'
    ),
    'google/tapas-base-finetuned-tabfact': (
        'https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'
    ),
}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	"tapas"






	def __init__(			self   :					List[Any]				,A   :					Dict=3_05_22				,A   :					int=7_68				,A   :					List[Any]=12				,A   :					Any=12				,A   :					str=30_72				,A   :					Dict="gelu"				,A   :					str=0.1				,A   :					Any=0.1				,A   :					Union[str, Any]=10_24				,A   :					Dict=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]				,A   :					str=0.02				,A   :					List[str]=1E-12				,A   :					List[Any]=0				,A   :					str=10.0				,A   :					Any=0				,A   :					Optional[int]=1.0				,A   :					Optional[Any]=None				,A   :					Any=1.0				,A   :					int=False				,A   :					str=None				,A   :					str=1.0				,A   :					Optional[Any]=1.0				,A   :					List[Any]=False				,A   :					Any=False				,A   :					Tuple="ratio"				,A   :					Optional[int]=None				,A   :					Any=None				,A   :					Optional[int]=64				,A   :					List[Any]=32				,A   :					int=False				,A   :					List[Any]=True				,A   :					str=False				,A   :					Optional[int]=False				,A   :					Any=True				,A   :					Tuple=False				,A   :					Tuple=None				,A   :					List[str]=None				,**A   :					Union[str, Any]				,):
					super().__init__(pad_token_id=A				,**A					)

					# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
					__A       =					vocab_size
					__A       =					hidden_size
					__A       =					num_hidden_layers
					__A       =					num_attention_heads
					__A       =					hidden_act
					__A       =					intermediate_size
					__A       =					hidden_dropout_prob
					__A       =					attention_probs_dropout_prob
					__A       =					max_position_embeddings
					__A       =					type_vocab_sizes
					__A       =					initializer_range
					__A       =					layer_norm_eps

					# Fine-tuning task hyperparameters
					__A       =					positive_label_weight
					__A       =					num_aggregation_labels
					__A       =					aggregation_loss_weight
					__A       =					use_answer_as_supervision
					__A       =					answer_loss_importance
					__A       =					use_normalized_answer_loss
					__A       =					huber_loss_delta
					__A       =					temperature
					__A       =					aggregation_temperature
					__A       =					use_gumbel_for_cells
					__A       =					use_gumbel_for_aggregation
					__A       =					average_approximation_function
					__A       =					cell_selection_preference
					__A       =					answer_loss_cutoff
					__A       =					max_num_rows
					__A       =					max_num_columns
					__A       =					average_logits_per_cell
					__A       =					select_one_column
					__A       =					allow_empty_column_selection
					__A       =					init_cell_selection_weights_to_zero
					__A       =					reset_position_index_per_cell
					__A       =					disable_per_token_loss

					# Aggregation hyperparameters
					__A       =					aggregation_labels
					__A       =					no_aggregation_label_index

					if isinstance(self.aggregation_labels				,A					):
									__A       =					{int(A					): v for k, v in aggregation_labels.items()}




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							'''ZinengTang/tvlt-base'''
         snake_case											=							tempfile.mkdtemp()
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltImageProcessor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self     ,   **__snake_case       ):
         return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,   **__snake_case       )
    def 						a_     (			self       ):
         shutil.rmtree(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )
         processor.save_pretrained(self.tmpdirname       )
         snake_case											=							TvltProcessor.from_pretrained(self.tmpdirname       )

         self.assertIsInstance(processor.feature_extractor     ,   __snake_case       )
         self.assertIsInstance(processor.image_processor     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )

         snake_case											=							feature_extractor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(audio=__snake_case     ,   return_tensors='''np'''       )

         for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							image_processor(__snake_case     ,   return_tensors='''np'''       )
         snake_case											=							processor(images=__snake_case     ,   return_tensors='''np'''       )

         for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()     ,   input_processor[key].sum()     ,   delta=1E-2       )
    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         snake_case											=							np.ones([1_2_0_0_0]       )
         snake_case											=							np.ones([3, 2_2_4, 2_2_4]       )

         snake_case											=							processor(audio=__snake_case     ,   images=__snake_case       )

         self.assertListEqual(list(inputs.keys()       )     ,   ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']       )

         # test if it raises when no input is passed
         with pytest.raises(__snake_case       ):
              processor()




    def 						a_     (			self       ):
         snake_case											=							self.get_image_processor()
         snake_case											=							self.get_feature_extractor()

         snake_case											=							TvltProcessor(image_processor=__snake_case     ,   feature_extractor=__snake_case       )

         self.assertListEqual(
             processor.model_input_names     ,   image_processor.model_input_names + feature_extractor.model_input_names     ,   msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''     ,   )




